1 Project One Carpoll. 2 3 Excel Convert categorical data into dummy variables Type of vehicle:...
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Transcript of 1 Project One Carpoll. 2 3 Excel Convert categorical data into dummy variables Type of vehicle:...
![Page 1: 1 Project One Carpoll. 2 3 Excel Convert categorical data into dummy variables Type of vehicle: family, sporty, work Sort type Select some of family.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d605503460f94a41cb2/html5/thumbnails/1.jpg)
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Project OneCarpoll
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Carpoll
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Excel Convert categorical data into dummy
variables Type of vehicle: family, sporty, work Sort type
Select some of family observations Expand selection in dialog box
Code family as one, sporty and work as zero
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Vehicle Type Family
Yes: One, 155 No: zero, 148 Total: 303
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Plot of Family Type (Zero/One) Vs. Age
y = 0.0196x - 0.0891
R2 = 0.0546
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0 10 20 30 40 50 60 70
Age
Pro
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Approximation to Cumulative Distribution Function Sigmoid
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Sort Gender and Code zero/one Male: one; Female: zero
Select some observations in sex, expand selection, sort
Ditto for marital status Married: one Single: zero
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Cross-Classification of Type with GenderFamily Sporty &
WorkTotal
Female 76 62 138
Male 79 86 165
Total 155 148 303
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#-Way classification in one step
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Family: YesMarried Single Total
Female 58 18 76
Male 61 18 79
Total 119 36 155
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Cut and Paste Fambern, age, gender, marital into EViews
File Menu New
workfile
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Workfile Dialog Box
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Generate x = 1
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Estimate Linear Probability Model
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Age and marital are significant,And have a positive effect on The probability of choosing aFamily car. Gender barely addsto the explanation and is negative
Given age and gender, being married increases the
Probability of favoring a family car by 0.23
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Married and Female
Single and Male
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![Page 31: 1 Project One Carpoll. 2 3 Excel Convert categorical data into dummy variables Type of vehicle: family, sporty, work Sort type Select some of family.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d605503460f94a41cb2/html5/thumbnails/31.jpg)
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Married and Female
Single and Male
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This probability model does a better job of explaining thoseWho favor a family car comparedTo those who don’t
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Logit Fit
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Married women
Single men
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Linear Probability Model Car Size Car size: Small, medium, large Varies with age, gender, and marital status
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Sort by Size and then by Age
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Contingency Table Analysis: ObservedSmall Medium Large Margin
18-29 70 59 15 149
30-39 59 58 21 138
40- 8 7 6 21
Margin 137 124 42 303
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Contingency Table Analysis: Small Medium Large Margin
18-29 149
30-39 138
40- 21
Margin 137 124 42 303
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Contingency Table Analysis: ExpectedSmall Medium Large Margin
18-29 67.4 61.0 20.7 149
30-39 62.4 56.5 19.1 138
40- 9.5 8.6 2.9 21
Margin 137 124 42 303
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Contingency Table Analysis: [Observed – Expected]2
Small Medium Large
18-29 6.76 4.0 32.49
30-39 11.56 2.25 3.61
40- 2.25 2.56 9.61
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0.00
0.05
0.10
0.15
0 5 10 15
CHIR
DE
NS
9.5
5%
2 =75.09
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Contingency Table Analysis: Small Medium Large
18-29 Fewer than exp
30-39 Fewer than exp
40- More than exp
Margin
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Summary
People in their 20’s prefer smaller cars People in their forties or older prefer large
cars
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Country Vs. Size Country: American, European, Japanese Size: small, medium, large
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Contingency Table Analysis: Observed Small Medium Large Margin
American 26 19 92 137
European 53 17 54 124
Japanese 36 4 2 42
Margin 115 40 148 303
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Contingency Table Analysis: Small Medium Large Margin
American 137
European 124
Japanese 42
Margin 115 40 148 303
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Contingency Table Analysis: Expected Small Medium Large Margin
American 52.0 18.1 66.9 137
European 47.1 16.4 60.6 124
Japanese 15.9 5.5 20.5 42
Margin 115 40 148 303
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Contingency Table Analysis: [Observed – Expected]2
Small Medium Large
American 676 0.81 630
European 34.8 0.36 43.6
Japanese 404 2.25 342
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Contingency Table Analysis: Small Medium Large
American fewer than exp
more
European
Japanese more fewer
Margin
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Summary Preference for large American cars and for
small Japanese cars.